Natural Hazards Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

This article is about the state of ICON principles Goldman et al. (2021), https://doi. org/10.1029/2021EO153180 in natural hazards and a discussion on the opportunities and challenges of adopting them. Natural hazards pose risks to society, infrastructure, and the environment. Hazard interactions and their cascading phenomena in space and time can further intensify the impacts. Natural hazards’ risks are expected to increase in the future due to environmental, demographic, and socioeconomic changes. It is important to quantify and effectively communicate risks to inform the design and implementation of risk mitigation and adaptation strategies. Multihazard multisector risk management poses several nontrivial challenges, including: (a) integrated risk assessment, (b) Earth system data-model fusion, (c) uncertainty quantification and communication, and (d) crossing traditional disciplinary boundaries. Here, we review these challenges, highlight current research and operational endeavors, and underscore diverse research opportunities. We emphasize the need for integrated approaches, coordinated processes, open science, and networked efforts (ICON) for multihazard multisector risk management

Rapid review of factors influencing dietary behaviors in Fiji

IntroductionIn Fiji, multiple burdens of malnutrition including undernutrition, overweight/obesity, and micronutrient deficiencies coexist at the individual, household, and population levels. The diets of children, adolescents, and adults are generally unhealthy. The objective of this review was to understand how the dietary behaviors of children, adolescents, and women in Fiji are influenced by individual, social, and food environment factors.MethodsThis rapid review was conducted to synthesize existing evidence, identify research gaps in the evidence base, and make recommendations for future research. The Cochrane Rapid Reviews Methods and the updated guideline for reporting systematic reviews were used. The search strategy for this rapid review was based on the Population Context Outcome [P(E)CO] framework, including search terms for population (children, adolescents, and adults), context (Fiji), and outcome (dietary behaviors). Searches were conducted in PubMed, Scopus, PsycINFO, and Google Scholar.ResultsThe 22 studies included in this review identified different factors influencing dietary behaviors in Fiji. Individual preferences for processed and imported foods, especially of younger generations, and social dynamics, especially gender norms and social pressure, to serve meat and overeat appeared to be prominent in driving dietary habits. The ongoing nutrition transition has led to increasing availability and affordability of ultra-processed and fast foods, especially in urban areas. Concerns about food safety and contamination and climate change and its effect on local food production also appear to influence dietary choices.DiscussionThis review identified different dynamics influencing dietary behaviors, but also research gaps especially with regard to the food environment, calling for an integrated approach to address these factors more systemically

COVID-19 vaccination up-take in three districts of Nepal

Vaccine hesitancy during the COVID-19 pandemic continues to be an issue in terms of global efforts to decrease transmission rates. Despite high demand for the vaccines in Nepal, the country still contends with challenges related to vaccine accessibility, equitable vaccine distribution, and vaccine hesitancy. Study objectives were to identify: 1) up-take and intention for use of COVID-19 vaccines, 2) factors associated with vaccine up-take, and 3) trusted communication strategies about COVID-19 and the vaccines. A quantitative survey was implemented in August and September 2021 through an initiative at the Nepali Ministry of Health and Population Department of Health Services, Family Welfare Division. Data were collected from 865 respondents in three provinces (Bagmati, Lumbini, and Province 1). Ordinal multivariate logistic regression was utilized to determine relationships between vaccination status and associated factors. Overall, 62% (537) respondents were fully vaccinated and 18% (159) were partially vaccinated. Those respondents with higher education (p \u3c .001) and higher household income (p \u3c .001) were more likely vaccinated. There were also significant differences in vaccine up-take across the three provinces (p \u3c .001). Respondents who were vaccinated were significantly more likely to perceive vaccines as efficacious in terms of preventing COVID-19 (p = .004) and preventing serious outcomes (p = .010). Among both vaccinated and unvaccinated individuals, there was a high level of trust in information about COVID-19 vaccines provided through local health-care workers [e.g. nurses and physicians]. These results are consistent with other findings within the South Asia region. Targeted advocacy and outreach efforts are needed to support ongoing COVID-19 vaccination campaigns throughout Nepal

Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Rainfall-induced landslides cause frequent disruptions to critical infrastructure in mountainous countries. Climate change is altering rainfall patterns and localizing extreme rainfall events, increasing the occurrence of landslides. For planning climate-resilient critical infrastructure in landslide-prone regions, it is urgent to understand the changing landslide susceptibility in relation to changing rainfall extremes and spatially overlay them with critical infrastructure to determine risk zones. As such, areas requiring financial reinforcements can be prioritized. In this paper, we develop a framework linking changing rainfall extremes to landslide susceptibility and intensity of critical infrastructure — exemplified on a national scale using Nepal as a case study. First, we define a set of 21 different unique rainfall indices that describe extreme and localized rainfall. Second, we prepare a new annual (2016–2020) inventory of 107,900 landslides in Nepal mapped on PlanetScope satellite imagery. Next, we prepare a landslide susceptibility map by training a random forest model using the collected extreme rainfall indices and landslide locations in combination with spatial data on topography. Fourth, we construct a gridded critical infrastructure spatial density map that quantifies the intensity of infrastructure (i.e., transportation, energy, telecommunication, waste, water, health, and education) at each grid location using OpenStreetMap. The landslide susceptibility map classified Nepal's topography into low (36 %), medium (33 %), and (32 %) high rainfall-triggered landslide susceptibility zones. The landslide susceptibility map had an average area under the receiver characteristic curve value of 0.94. Finally, we overlay the landslide susceptibility map with the critical infrastructure intensity to identify areas needing financial reinforcement. Our framework reasonably mapped critical infrastructure hotspots in Nepal prone to landslides on a 1 km grid. The hotspots are mainly concentrated along major national highways and in provinces 4, 3, and 1, highlighting the need for improved land management practices. These hotspots need spatial prioritization regarding climate-resilient critical infrastructure financing and slope conservation policies. The research data, output maps, and code are publicly released via an ArcGIS WebApp and GitHub repository. The framework is scalable and can be used for developing infrastructure financing strategies for landslide mountain regions and countries

Vegetation loss and recovery analysis from the 2015 Gorkha earthquake (7.8 Mw) triggered landslides

The 2015 Gorkha earthquake (7.8 Mw) triggered thousands of landslides in the highlands of central Nepal, causing widespread vegetation damage. After the earthquake, several attempts were made by the government to recover damaged vegetation; however, the efficacy of artificial restoration (from public finance) vs. self-ecological restoration is unknown. We analyze the vegetation recovery process of the areas impacted by the 2015 Gorkha earthquake landslides with a dual-lens: (1) remote sensing and (2) public finance and policy. Using remote sensing, Vegetation Recovery Rate (VRR) is estimated from the normalized difference vegetation index (NDVI) from Landsat imagery between 2015 and 2021. Then public finance data is analyzed to compare the efficacy of vegetation recovery from the artificial vs. self-ecological restoration. The study examines fourteen severely impacted districts from the Gorkha earthquake in 2015. Out of 24,826 landslides triggered by the earthquake, ~95% of vegetation damage was caused by 13,670 large landslides (with area >0.09 ha). A total of 8651.58 ha of vegetation was lost due to landslides induced by the 2015 Gorkha earthquake. About 4442 ha (51%) of such lost vegetation has been restored so far. Only 9.5% of this restored vegetation was due to artificial restoration, while the remaining 90.5% was by self-ecological restoration process in protected areas. Furthermore, VRR analysis showed that at least nine years are required to restore vegetation cover to the pre-earthquake level (R2 =0.91). The government had invested 3.73 million USD in this duration for artificial restoration. Our findings suggest that strict protection promotes self-ecological restoration, an effective tract for vegetation recovery, over artificial interventions. Findings provide insights for plausible decision-making in restoring lost vegetation due to earthquake-triggered landslides

Assessment of shelter location-allocation for multi-hazard emergency evacuation

Intense rainstorms often trigger multiple disasters in mountain regions, such as floods and landslides. In disaster planning, the local administration allocates nearby schools or open fields as emergency evacuation shelters. However, access to these shelters is often cut off for certain population clusters during disaster impact on routes. We develop a framework for selecting emergency evacuation shelter locations for multi-disaster impact planning (floods and landslides). The framework consists of two parts. Firstly, we develop susceptibility maps of individual hazards using Random Forest algorithm and Google earth engine. Secondly, we assess the shelter's location-allocation by implementing two models in GIS: P-median and maximal covering location problem. Our framework treats existing schools as evacuation shelters and individual households as demand points in an emergency. The P-median method finds the shelter locations by minimizing maximum distances between the households. The maximal covering location problem method evaluates the coverage of households by the facilities of the evacuation shelters within an impedance cutoff. We tested our work in a mountainous village in the Western Ghat region, India, by recreating the 2005 rainstorm disaster that caused more than 190 fatalities and damaged 400 households. The result shows that existing shelters are insufficient to provide services to all households within 30 min and 60 min. This methodology helps develop simultaneous-hazard impact plans by local administration units in mountain regions to ensure emergency facilities' safe operation.info:eu-repo/semantics/publishedVersio

Assessment of shelter location-allocation for multi-hazard emergency evacuation

Intense rainstorms often trigger multiple disasters in mountain regions, such as floods and landslides. In disaster planning, the local administration allocates nearby schools or open fields as emergency evacuation shelters. However, access to these shelters is often cut off for certain population clusters during disaster impact on routes. We develop a framework for selecting emergency evacuation shelter locations for multi-disaster impact planning (floods and landslides). The framework consists of two parts. Firstly, we develop susceptibility maps of individual hazards using Random Forest algorithm and Google earth engine. Secondly, we assess the shelter's location-allocation by implementing two models in GIS: P-median and maximal covering location problem. Our framework treats existing schools as evacuation shelters and individual households as demand points in an emergency. The P-median method finds the shelter locations by minimizing maximum distances between the households. The maximal covering location problem method evaluates the coverage of households by the facilities of the evacuation shelters within an impedance cutoff. We tested our work in a mountainous village in the Western Ghat region, India, by recreating the 2005 rainstorm disaster that caused more than 190 fatalities and damaged 400 households. The result shows that existing shelters are insufficient to provide services to all households within 30 minutes and 60 minutes. This methodology helps develop simultaneous hazard impact plans by local administration units in mountain regions to ensure emergency facilities' safe operation

Insights on the Impacts of Hydroclimatic Extremes and Anthropogenic Activities on Sediment Yield of a River Basin

Streamflow and sediment flux variations in a mountain river basin directly affect the downstream biodiversity and ecological processes. Precipitation is expected to be one of the main drivers of these variations in the Himalayas. However, such relations have not been explored for the mountain river basin, Nepal. This paper explores the variation in streamflow and sediment flux from 2006 to 2019 in central Nepal’s Kali Gandaki River basin and correlates them to precipitation indices computed from 77 stations across the basin. Nine precipitation indices and four other ratio-based indices are used for comparison. Percentage contributions of maximum 1-day, consecutive 3-day, 5-day and 7-day precipitation to the annual precipitation provide information on the severity of precipitation extremeness. We found that maximum suspended sediment concentration had a significant positive correlation with the maximum consecutive 3-day precipitation. In contrast, average suspended sediment concentration had significant positive correlations with all ratio-based precipitation indices. The existing sediment erosion trend, driven by the amount, intensity, and frequency of extreme precipitation, demands urgency in sediment source management on the Nepal Himalaya’s mountain slopes. The increment in extreme sediment transports partially resulted from anthropogenic interventions, especially landslides triggered by poorly-constructed roads, and the changing nature of extreme precipitation driven by climate variability

Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learning

Groundwater is a critical resource, yet its detailed assessment in mountainous regions is challenged by varying topography, complex hydrogeological characteristics and limited data. In this study, machine learning approaches were used to analyze the groundwater potential in five different watersheds in Nepal. Explainable machine learning models (EBM and GAMI-net) were used to identify zones with different groundwater potentials and controlling factors. The models were validated with k-fold cross-validation using the area under the receiver operating characteristics curve for the two groundwater potential models with unseen validation dataset of 0.87 and 0.88 respectively. We found that precipitation, elevation, soil bulk density, slope and lineaments primarily controls the groundwater potential in the study regions. The expected impact of each of the factors on groundwater potential was complex and multimodal. The results of this study can be used to improve water resource management and ensure sustainable groundwater use in the region.</p